Do Social Networks Solve Information Problems for Peer-to-Peer Lending? Evidence from Prosper.Com

Abstract

This paper proposes a holistic view of a network organization's computing environment to examine computer virus propagation patterns. We empirically examine a large-scale organizational network consisting of both social network and technological network. By applying information retrieval techniques, we map nodes in the social network to nodes in the technological network to construct the composite network of the organization. We apply social network analysis to study the topologies of social and technological networks in this organization. We statistically test the impact of the interplay between social and technological network on computer virus propagation using a susceptible-infective-recovered epidemic process. We find that computer viruses propagate faster but reach lower level of infection through technological network than through social network, and viruses propagate the fastest and reach the highest level of infection through the composite network. Overlooking the interplay of social network and technological network underestimates the virus propagation speed and the scale of infection.Key words: social network analysis, interplay between social and technological networks, computer viruses 1 We gratefully acknowledge financial support from the NET Institute (www.netinst.org) and the Kauffman Foundation.3

Who can get money? Evidence from the Chinese peer-to-peer lending platform

“…Berger and Gleisner (2009) Chen et al (2009) reveal that the borrowers' general group social capital (i.e. group membership) improves funding success and reduces interest rates and default probability, while Freedman and Jin (2008) find that loans endorsed by friends, and bids, show fewer missed bids have fewer missed) payments and higher rates of return. Using a sample of Prosper listings, Lin et al (2013) examine the role that friendship networks played in the outcomes of loans on the P2P market.…”

“…One of the major problems of P2P lending is asymmetric information (Freedman and Jin, 2008;Lin et al, 2013). This problem is more pronounced in the Chinese P2P lending market due to the lack of reliable and comprehensive credit information.…”

“…Freedman and Jin, 2008;Weiss et al 2010;Pope and Sydnor, 2011;Ravina, 2008;Lin et al 2013;Iyer et al 2016) and crowdfunding, another type of alternative financing activities (e.g. Belleflamme et al 2015;Colombo et al 2015;.…”

Rational Herding in Microloan Markets

“…(Ravina 2008), lender learning from hard versus soft information (Iyer et al 2009), perceived trustworthiness (Duarte, Siegel and Young 2010), borrowers' identity claims (Herzenstein, Sonenshein, and Dholakia 2011), taste-based discrimination (Pope and Sydnor 2011), and interest rate caps (Rigbi 2011). Another stream of research examines the social network effects of Prosper, such as how friend endorsements affect loan performance (Freedman and Jin 2008), how borrowers' group affiliations relate to loan default risk (Everett 2010), how the strength and verifiability of relational network measures influence funding outcomes and loan defaults (Lin, Viswanathan and Prabhala 2011), and how participation in online communities changes lenders' risk preferences (Zhu et al 2011 we record a set of its attributes and monitor its funding progression, including the amount of funding it has received, the number of bids, and its current interest rate. Beyond listing attributes, Table 2 also presents summary statistics on lending activities on the first day and last day of the listing period.…”

Information Sharing and User Behavior in Internet-enabled Peer-to-peer Lending Systems: An Empirical Study

“…These studies assume that the social networks associated with borrowers, as well as the identity of borrowers within those networks, play an important role in explaining both lenders' investment decisions and borrowers' likelihood of repaying that investment. This assumption is supported by observations of lending and repayment behavior within the explicit borrower groupings facilitated on Prosper (Freedman & Jin 2008, Herrero-Lopez 2009, Lin 2009). In these contexts, greater investment occurred because lenders made inferences about the identity of the borrower based upon known characteristics of the group, while greater repayment occurred as a result of the borrowers' commitment to that group and consequently, their higher susceptibility to the normative pressures to repay.…”